Efficient Inverse Covariance Matrix Estimation for Low-Complexity Closed-Loop DPD Systems

Pablo Pascual Campo, Lauri Anttila, Vesa Lampu, Yan Guo, Neng Wang, Mikko Valkama

Tutkimustuotos: KonferenssiartikkeliScientificvertaisarvioitu

9 Lataukset (Pure)

Abstrakti

This paper studies closed-loop digital predistortion systems, with special focus on linearization of mmW active antenna arrays. Considering the beam-dependent nonlinear distortion and very high DPD processing rates, a modified self-orthogonalized (SO) learning solution is proposed, which is capable of reducing the computational complexity compared to other similar solutions, while at the same time obtaining a comparable linearization performance. The modified SO consists of a novel method for efficiently calculating the inverse of the input data covariance matrix. Thorough RF measurement results at 28 GHz band featuring a state-of-the-art 64 element active array and channel bandwidths up to 800 MHz, are reported. A complexity analysis is also carried out which, together with the obtained results, allow to asses the performance-complexity trade-offs. Altogether, the results show that the proposed methods can facilitate efficient mmW active antenna array linearization.
AlkuperäiskieliEnglanti
Otsikko2021 IEEE MTT-S International Wireless Symposium (IWS)
KustantajaIEEE
Sivumäärä3
ISBN (elektroninen)978-1-6654-3527-7
ISBN (painettu)978-1-6654-3528-4
DOI - pysyväislinkit
TilaJulkaistu - 2021
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE International Wireless Symposium - Nanjing, Kiina
Kesto: 23 toukok. 202126 toukok. 2021

Conference

ConferenceIEEE International Wireless Symposium
Maa/AlueKiina
KaupunkiNanjing
Ajanjakso23/05/2126/05/21

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